Pennsylvania State University

Course Descripton

Course Staff

The Spring 2020 offering of Computational Foudations of Informatics is taught by Professor Vasant Honavar.

Course Schedule

Lectures: Monday, Wednesday - 12:20pm, 206E Westgate Building

Office Hours:

  Instructor: Vasant Honavar: Monday 4:00pm - 5:00pm,

  Teaching Assistant: Thanh Le: Thursday 4:00pm-5pm

Course Overview

This course provides the conceptual and theoretical foundations of informatics, with particular emphasis on the role of computational or algorithmic abstractions in artificial intelligence, cognitive and brain sciences, social, behavioral and economic sciences, and life sciences, and the humanities. In each case, concrete examples will be used to illustrate how fundamental questions in the domain can be translated into algorithmic problems that can then be solved using concepts, methods, and tools of computer science. Through problem sets, laboratory projects, and assignments that sample these topics, students acquire an understanding of how to devise and study algorithmic abstractions of problems in artificial intelligence (e.g., learning and reasoning), cognitive sciences (relating brain activity to behavior), social sciences (rational behavior, collective decision making), life sciences (gene regulation) and the humanities (analysis of cultural artifacts). Upon completion of this course, the students will have a understanding of the fundamental role and the utility of computational abstractions across a broad range of disciplines. They will be able to iteratively formulate and answer questions in one or more domains using computational abstractions. They will have an understanding of the capabilities and limitations of algorithmic abstractions of various kinds. They will be able to empirically evaluate the utility of specific algorithmic abstractions. They will gain hands-on experience in developing and using algorithmic abstractions in one or more areas.

Target Audience

This course is designed as a core course for PhD students in Informatics. The course is intentionally designed to be accessible to graduate students from a broad range of academic backgrounds. Students with prior training in computer science will learn how concepts, ideas, and tools from computer science that they are familiar with can be used to answer questions in a wide range of scientific disciplines, and even the humanities. Students with prior training in a discipline other than computer science e.g., life sciences, cognitive sciences, social sciences, etc. will learn how to formulate and use algorithmic abstractions of their respective domains of enquiry in their respective disciplines and use them to answer key scientific questions in those disciplines. Thus, the course is likely to be of interest to graduate students from a broad range of other disciplines who are interested in learning about how algorithmic (information processing) abstractions can be used to formulate and answer questions across a broad range of scientific disciplines, e.g., cognitive and brain sciences, biological sciences, social sciences, and even the humanities.

If you are not sure whether you have the necessary background, please talk to the instructor.